Initiating A Collective Operation In A Parallel Computer

ABSTRACT

Initiating a collective operation in a parallel computer that includes compute nodes coupled for data communications and organized in an operational group for collective operations with one compute node assigned as a root node, including: identifying, by a non-root compute node, a collective operation to execute in the operational group of compute nodes; initiating, by the non-root compute node, execution of the collective operation amongst the compute nodes of the operational group including: sending, by the non-root compute node to one or more of the other compute nodes in the operational group, an active message, the active message including information configured to initiate execution of the collective operation amongst the compute nodes of the operational group; and executing, by the compute nodes of the operational group, the collective operation.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The field of the invention is data processing, or, more specifically,methods, apparatus, and products for initiating a collective operationin a parallel computer.

2. Description of Related Art

The development of the EDVAC computer system of 1948 is often cited asthe beginning of the computer era. Since that time, computer systemshave evolved into extremely complicated devices. Today's computers aremuch more sophisticated than early systems such as the EDVAC. Computersystems typically include a combination of hardware and softwarecomponents, application programs, operating systems, processors, buses,memory, input/output devices, and so on. As advances in semiconductorprocessing and computer architecture push the performance of thecomputer higher and higher, more sophisticated computer software hasevolved to take advantage of the higher performance of the hardware,resulting in computer systems today that are much more powerful thanjust a few years ago.

Parallel computing is an area of computer technology that hasexperienced advances. Parallel computing is the simultaneous executionof the same task (split up and specially adapted) on multiple processorsin order to obtain results faster. Parallel computing is based on thefact that the process of solving a problem usually can be divided intosmaller tasks, which may be carried out simultaneously with somecoordination.

Parallel computers execute parallel algorithms. A parallel algorithm canbe split up to be executed a piece at a time on many differentprocessing devices, and then put back together again at the end to get adata processing result. Some algorithms are easy to divide up intopieces. Splitting up the job of checking all of the numbers from one toa hundred thousand to see which are primes could be done, for example,by assigning a subset of the numbers to each available processor, andthen putting the list of positive results back together. In thisspecification, the multiple processing devices that execute theindividual pieces of a parallel program are referred to as ‘computenodes.’ A parallel computer is composed of compute nodes and otherprocessing nodes as well, including, for example, input/output (‘I/O’)nodes, and service nodes.

Parallel algorithms are valuable because it is faster to perform somekinds of large computing tasks via a parallel algorithm than it is via aserial (non-parallel) algorithm, because of the way modern processorswork. It is far more difficult to construct a computer with a singlefast processor than one with many slow processors with the samethroughput. There are also certain theoretical limits to the potentialspeed of serial processors. On the other hand, every parallel algorithmhas a serial part and so parallel algorithms have a saturation point.After that point adding more processors does not yield any morethroughput but only increases the overhead and cost.

Parallel algorithms are designed also to optimize one more resource thedata communications requirements among the nodes of a parallel computer.There are two ways parallel processors communicate, shared memory ormessage passing. Shared memory processing needs additional locking forthe data and imposes the overhead of additional processor and bus cyclesand also serializes some portion of the algorithm.

Message passing processing uses high-speed data communications networksand message buffers, but this communication adds transfer overhead onthe data communications networks as well as additional memory need formessage buffers and latency in the data communications among nodes.Designs of parallel computers use specially designed data communicationslinks so that the communication overhead will be small but it is theparallel algorithm that decides the volume of the traffic.

Many data communications network architectures are used for messagepassing among nodes in parallel computers. Compute nodes may beorganized in a network as a ‘torus’ or ‘mesh,’ for example. Also,compute nodes may be organized in a network as a tree. A torus networkconnects the nodes in a three-dimensional mesh with wrap around links.Every node is connected to its six neighbors through this torus network,and each node is addressed by its x,y,z coordinate in the mesh. In sucha manner, a torus network lends itself to point to point operations. Ina tree network, the nodes typically are connected into a binary tree:each node has a parent, and two children (although some nodes may onlyhave zero children or one child, depending on the hardwareconfiguration). Although a tree network typically is inefficient inpoint to point communication, a tree network does provide high bandwidthand low latency for certain collective operations, message passingoperations where all compute nodes participate simultaneously, such as,for example, an allgather operation. In computers that use a torus and atree network, the two networks typically are implemented independentlyof one another, with separate routing circuits, separate physical links,and separate message buffers.

SUMMARY OF THE INVENTION

Methods, apparatus, and products for initiating a collective operationin a parallel computer are disclosed in this specification. The parallelcomputer includes a plurality of compute nodes coupled for datacommunications by one or more data communications networks. The computenodes organized in an operational group for collective operations withone compute node assigned as a root node of the operational group.Initiating a collective operation in accordance with embodiments of thepresent invention includes: identifying, by a non-root compute node, acollective operation to execute in the operational group of computenodes; initiating, by the non-root compute node, execution of thecollective operation amongst the compute nodes of the operational groupincluding: sending, by the non-root compute node to one or more of theother compute nodes in the operational group, an active message, theactive message including information configured to initiate execution ofthe collective operation amongst the compute nodes of the operationalgroup; and executing, by the compute nodes of the operational group, thecollective operation.

The foregoing and other objects, features and advantages of theinvention will be apparent from the following more particulardescriptions of exemplary embodiments of the invention as illustrated inthe accompanying drawings wherein like reference numbers generallyrepresent like parts of exemplary embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary system for initiating a collectiveoperation in a parallel computer according to embodiments of the presentinvention.

FIG. 2 sets forth a block diagram of an example compute node useful in aparallel computer capable of initiating a collective operation accordingto embodiments of the present invention.

FIG. 3A sets forth a block diagram of an example Point-To-Point Adapteruseful in systems for initiating a collective operation in a parallelcomputer according to embodiments of the present invention.

FIG. 3B sets forth a block diagram of an example Global CombiningNetwork Adapter useful in systems for initiating a collective operationin a parallel computer according to embodiments of the presentinvention.

FIG. 4 sets forth a line drawing illustrating an example datacommunications network optimized for point-to-point operations useful insystems capable of initiating a collective operation in a parallelcomputer according to embodiments of the present invention.

FIG. 5 sets forth a line drawing illustrating an example globalcombining network useful in systems capable of initiating a collectiveoperation in a parallel computer according to embodiments of the presentinvention.

FIG. 6 sets forth a flow chart illustrating an example method forinitiating a collective operation in a parallel computer according toembodiments of the present invention.

FIG. 7 sets forth a flow chart illustrating another example methodinitiating a collective operation in a parallel computer according toembodiments of the present invention.

FIG. 8 sets forth a flow chart illustrating another example methodinitiating a collective operation in a parallel computer according toembodiments of the present invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Exemplary methods, apparatus, and products for initiating a collectiveoperation in a parallel computer in accordance with the presentinvention are described with reference to the accompanying drawings,beginning with FIG. 1. FIG. 1 illustrates an exemplary system forinitiating a collective operation in a parallel computer according toembodiments of the present invention. The system of FIG. 1 includes aparallel computer (100), non-volatile memory for the computer in theform of a data storage device (118), an output device for the computerin the form of a printer (120), and an input/output device for thecomputer in the form of a computer terminal (122).

The parallel computer (100) in the example of FIG. 1 includes aplurality of compute nodes (102). The compute nodes (102) are coupledfor data communications by several independent data communicationsnetworks including a high speed Ethernet network (174), a Joint TestAction Group (‘JTAG’) network (104), a global combining network (106)which is optimized for collective operations using a binary tree networktopology, and a point-to-point network (108), which is optimized forpoint-to-point operations using a torus network topology. The globalcombining network (106) is a data communications network that includesdata communications links connected to the compute nodes (102) so as toorganize the compute nodes (102) as a binary tree. Each datacommunications network is implemented with data communications linksamong the compute nodes (102). The data communications links providedata communications for parallel operations among the compute nodes(102) of the parallel computer (100).

The compute nodes (102) of the parallel computer (100) are organizedinto at least one operational group (132) of compute nodes forcollective parallel operations on the parallel computer (100). Eachoperational group (132) of compute nodes is the set of compute nodesupon which a collective parallel operation executes. Each compute nodein the operational group (132) is assigned a unique rank that identifiesthe particular compute node in the operational group (132). Collectiveoperations are implemented with data communications among the computenodes of an operational group. Collective operations are those functionsthat involve all the compute nodes of an operational group (132). Acollective operation is an operation, a message-passing computer programinstruction that is executed simultaneously, that is, at approximatelythe same time, by all the compute nodes in an operational group (132) ofcompute nodes. Such an operational group (132) may include all thecompute nodes (102) in a parallel computer (100) or a subset all thecompute nodes (102). Collective operations are often built aroundpoint-to-point operations. A collective operation requires that allprocesses on all compute nodes within an operational group (132) callthe same collective operation with matching arguments. A ‘broadcast’ isan example of a collective operation for moving data among compute nodesof an operational group. A ‘reduce’ operation is an example of acollective operation that executes arithmetic or logical functions ondata distributed among the compute nodes of an operational group (132).An operational group (132) may be implemented as, for example, an MPI‘communicator.’

‘MPI’ refers to ‘Message Passing Interface,’ a prior art parallelcommunications library, a module of computer program instructions fordata communications on parallel computers. Examples of prior-artparallel communications libraries that may be improved for use insystems configured according to embodiments of the present inventioninclude MPI and the ‘Parallel Virtual Machine’ (‘PVM’) library. PVM wasdeveloped by the University of Tennessee, The Oak Ridge NationalLaboratory and Emory University. MPI is promulgated by the MPI Forum, anopen group with representatives from many organizations that define andmaintain the MPI standard. MPI at the time of this writing is a de factostandard for communication among compute nodes running a parallelprogram on a distributed memory parallel computer. This specificationsometimes uses MPI terminology for ease of explanation, although the useof MPI as such is not a requirement or limitation of the presentinvention.

Some collective operations have a single originating or receivingprocess running on a particular compute node in an operational group(132). For example, in a ‘broadcast’ collective operation, the processon the compute node that distributes the data to all the other computenodes is an originating process. In a ‘gather’ operation, for example,the process on the compute node that received all the data from theother compute nodes is a receiving process. The compute node on whichsuch an originating or receiving process runs is referred to as alogical root.

Most collective operations are variations or combinations of four basicoperations: broadcast, gather, scatter, and reduce. The interfaces forthese collective operations are defined in the MPI standards promulgatedby the MPI Forum. Algorithms for executing collective operations,however, are not defined in the MPI standards. In a broadcast operation,all processes specify the same root process, whose buffer contents willbe sent. Processes other than the root specify receive buffers. Afterthe operation, all buffers contain the message from the root process.

A scatter operation, like the broadcast operation, is also a one-to-manycollective operation. In a scatter operation, the logical root dividesdata on the root into segments and distributes a different segment toeach compute node in the operational group (132). In scatter operation,all processes typically specify the same receive count. The sendarguments are only significant to the root process, whose bufferactually contains sendcount*N elements of a given datatype, where N isthe number of processes in the given group of compute nodes. The sendbuffer is divided and dispersed to all processes (including the processon the logical root). Each compute node is assigned a sequentialidentifier termed a ‘rank.’ After the operation, the root has sentsendcount data elements to each process in increasing rank order. Rank 0receives the first sendcount data elements from the send buffer. Rank 1receives the second sendcount data elements from the send buffer, and soon.

A gather operation is a many-to-one collective operation that is acomplete reverse of the description of the scatter operation. That is, agather is a many-to-one collective operation in which elements of adatatype are gathered from the ranked compute nodes into a receivebuffer in a root node.

A reduction operation is also a many-to-one collective operation thatincludes an arithmetic or logical function performed on two dataelements. All processes specify the same ‘count’ and the same arithmeticor logical function. After the reduction, all processes have sent countdata elements from compute node send buffers to the root process. In areduction operation, data elements from corresponding send bufferlocations are combined pair-wise by arithmetic or logical operations toyield a single corresponding element in the root process' receivebuffer. Application specific reduction operations can be defined atruntime. Parallel communications libraries may support predefinedoperations. MPI, for example, provides the following pre-definedreduction operations:

MPI_MAX maximum MPI_MIN minimum MPI_SUM sum MPI_PROD product MPI_LANDlogical and MPI_BAND bitwise and MPI_LOR logical or MPI_BOR bitwise orMPI_LXOR logical exclusive or MPI_BXOR bitwise exclusive or

In addition to compute nodes, the parallel computer (100) includesinput/output (‘I/O’) nodes (110, 114) coupled to compute nodes (102)through the global combining network (106). The compute nodes (102) inthe parallel computer (100) may be partitioned into processing sets suchthat each compute node in a processing set is connected for datacommunications to the same I/O node. Each processing set, therefore, iscomposed of one I/O node and a subset of compute nodes (102). The ratiobetween the number of compute nodes to the number of I/O nodes in theentire system typically depends on the hardware configuration for theparallel computer (102). For example, in some configurations, eachprocessing set may be composed of eight compute nodes and one I/O node.In some other configurations, each processing set may be composed ofsixty-four compute nodes and one I/O node. Such example are forexplanation only, however, and not for limitation. Each I/O nodeprovides I/O services between compute nodes (102) of its processing setand a set of I/O devices. In the example of FIG. 1, the I/O nodes (110,114) are connected for data communications I/O devices (118, 120, 122)through local area network (‘LAN’) (130) implemented using high-speedEthernet.

The parallel computer (100) of FIG. 1 also includes a service node (116)coupled to the compute nodes through one of the networks (104). Servicenode (116) provides services common to pluralities of compute nodes,administering the configuration of compute nodes, loading programs intothe compute nodes, starting program execution on the compute nodes,retrieving results of program operations on the compute nodes, and soon. Service node (116) runs a service application (124) and communicateswith users (128) through a service application interface (126) that runson computer terminal (122).

The parallel computer (100) of FIG. 1 operates generally for initiatinga collective operation in a parallel computer in accordance withembodiments of the present invention. The compute nodes (102) of theexample parallel computer (100) are organized for in an operationalgroup (132) for collective operations. In the group (132), one computenode is assigned as a root node. Parallel computers of the prior arttypically implement collective operations in way that requires eachcompute node to post or call the same collective operation at the samepoint in execution. As such, a non-root compute node in prior artparallel computers cannot initiate execution of a collective operation.

In the example parallel computer (100) of FIG. 1, the compute nodesoperate in accordance with embodiments of the present invention toenable a non-root compute node to initiate a collective operation by:identifying, by a non-root compute node, a collective operation toexecute in the operational group (132) of compute nodes (102);initiating, by the non-root compute node, execution of the collectiveoperation amongst the compute nodes of the operational group (132) bysending, by the non-root compute node to one or more of the othercompute nodes in the operational group, an active message, where theactive message includes information configured to initiate execution ofthe collective operation amongst the compute nodes of the operationalgroup; and executing, by the compute nodes of the operational group, thecollective operation. An active message as the term is used here refersto a message that implements one or more callback functions to advise ofmessage dispatch and instruction completion and so on. From theperspective of the receiver, an active message effects a task, such asexecuting a set of instructions or a calling a function. In this way, anon-root compute node may initiate a collective operation, ad hoc ondemand, without requiring all other compute nodes to post or call thesame collective operation at the same point in execution.

Initiating a collective operation according to embodiments of thepresent invention is generally implemented on a parallel computer thatincludes a plurality of compute nodes organized for collectiveoperations through at least one data communications network. In fact,such computers may include thousands of such compute nodes. Each computenode is in turn itself a kind of computer composed of one or morecomputer processing cores, its own computer memory, and its owninput/output adapters. For further explanation, therefore, FIG. 2 setsforth a block diagram of an example compute node (102) useful in aparallel computer capable of initiating a collective operation accordingto embodiments of the present invention. The compute node (102) of FIG.2 includes a plurality of processing cores (165) as well as RAM (156).The processing cores (165) of FIG. 2 may be configured on one or moreintegrated circuit dies. Processing cores (165) are connected to RAM(156) through a high-speed memory bus (155) and through a bus adapter(194) and an extension bus (168) to other components of the computenode.

Stored in RAM (156) is an application program (226), a module ofcomputer program instructions that carries out parallel, user-level dataprocessing using parallel algorithms. In the example of FIG. 2, theapplication (226) may implement a participant in an operational group,such as a rank in an MPI-style communicator. Consider, for purposes ofexplanation, that the application (226) represents a non-rootparticipant in the operational group. To that end, the compute node(102) in the example of FIG. 2 is a non-root compute node. Execution ofthe application (226) causes the example compute node (102) of FIG. 2 toinitiate a collective operation in a parallel computer in accordancewith embodiments of the present invention. The compute node (102) maycarry out such collective operation initiation by: identifying, by thenon-root compute node (102), a collective operation (228) to execute inthe operational group of compute nodes; initiating, by the non-rootcompute node (102), execution of the collective operation (228) amongstthe compute nodes of the operational group by sending, to one or more ofthe other compute nodes in the operational group, an active message(230). The active message includes information configured to initiateexecution of the collective operation amongst the compute nodes of theoperational group. Finally, the compute nodes of the operational groupexecute the collective operation.

Also stored RAM (156) is a parallel communications library (161), alibrary of computer program instructions that carry out parallelcommunications among compute nodes, including point-to-point operationsas well as collective operations. A library of parallel communicationsroutines may be developed from scratch for use in systems according toembodiments of the present invention, using a traditional programminglanguage such as the C programming language, and using traditionalprogramming methods to write parallel communications routines that sendand receive data among nodes on two independent data communicationsnetworks. Alternatively, existing prior art libraries may be improved tooperate according to embodiments of the present invention. Examples ofprior-art parallel communications libraries include the ‘Message PassingInterface’ (‘MPI’) library and the ‘Parallel Virtual Machine’ (‘PVM’)library.

Also stored in RAM (156) is an operating system (162), a module ofcomputer program instructions and routines for an application program'saccess to other resources of the compute node. It is typical for anapplication program and parallel communications library in a computenode of a parallel computer to run a single thread of execution with nouser login and no security issues because the thread is entitled tocomplete access to all resources of the node. The quantity andcomplexity of tasks to be performed by an operating system on a computenode in a parallel computer therefore are smaller and less complex thanthose of an operating system on a serial computer with many threadsrunning simultaneously. In addition, there is no video I/O on thecompute node (102) of FIG. 2, another factor that decreases the demandson the operating system. The operating system (162) may therefore bequite lightweight by comparison with operating systems of generalpurpose computers, a pared down version as it were, or an operatingsystem developed specifically for operations on a particular parallelcomputer. Operating systems that may usefully be improved, simplified,for use in a compute node include UNIX™, Linux™, Windows XP™, AIX™,IBM's i5/OS™, and others as will occur to those of skill in the art.

The example compute node (102) of FIG. 2 includes several communicationsadapters (172, 176, 180, 188) for implementing data communications withother nodes of a parallel computer. Such data communications may becarried out serially through RS-232 connections, through external busessuch as USB, through data communications networks such as IP networks,and in other ways as will occur to those of skill in the art.Communications adapters implement the hardware level of datacommunications through which one computer sends data communications toanother computer, directly or through a network. Examples ofcommunications adapters useful in apparatus useful for initiating acollective operation in a parallel computer include modems for wiredcommunications, Ethernet (IEEE 802.3) adapters for wired networkcommunications, and 802.11b adapters for wireless networkcommunications.

The data communications adapters in the example of FIG. 2 include aGigabit Ethernet adapter (172) that couples example compute node (102)for data communications to a Gigabit Ethernet (174). Gigabit Ethernet isa network transmission standard, defined in the IEEE 802.3 standard,that provides a data rate of 1 billion bits per second (one gigabit).Gigabit Ethernet is a variant of Ethernet that operates over multimodefiber optic cable, single mode fiber optic cable, or unshielded twistedpair.

The data communications adapters in the example of FIG. 2 include a JTAGSlave circuit (176) that couples example compute node (102) for datacommunications to a JTAG Master circuit (178). JTAG is the usual nameused for the IEEE 1149.1 standard entitled Standard Test Access Port andBoundary-Scan Architecture for test access ports used for testingprinted circuit boards using boundary scan. JTAG is so widely adaptedthat, at this time, boundary scan is more or less synonymous with JTAG.JTAG is used not only for printed circuit boards, but also forconducting boundary scans of integrated circuits, and is also useful asa mechanism for debugging embedded systems, providing a convenientalternative access point into the system. The example compute node ofFIG. 2 may be all three of these: It typically includes one or moreintegrated circuits installed on a printed circuit board and may beimplemented as an embedded system having its own processing core, itsown memory, and its own I/O capability. JTAG boundary scans through JTAGSlave (176) may efficiently configure processing core registers andmemory in compute node (102) for use in dynamically reassigning aconnected node to a block of compute nodes useful in systems forinitiating a collective operation in a parallel computer to embodimentsof the present invention.

The data communications adapters in the example of FIG. 2 include aPoint-To-Point Network Adapter (180) that couples example compute node(102) for data communications to a network (108) that is optimal forpoint-to-point message passing operations such as, for example, anetwork configured as a three-dimensional torus or mesh. ThePoint-To-Point Adapter (180) provides data communications in sixdirections on three communications axes, x, y, and z, through sixbidirectional links: +x (181), −x (182), +y (183), −y (184), +z (185),and −z (186).

The data communications adapters in the example of FIG. 2 include aGlobal Combining Network Adapter (188) that couples example compute node(102) for data communications to a global combining network (106) thatis optimal for collective message passing operations such as, forexample, a network configured as a binary tree. The Global CombiningNetwork Adapter (188) provides data communications through threebidirectional links for each global combining network (106) that theGlobal Combining Network Adapter (188) supports. In the example of FIG.2, the Global Combining Network Adapter (188) provides datacommunications through three bidirectional links for global combiningnetwork (106): two to children nodes (190) and one to a parent node(192).

The example compute node (102) includes multiple arithmetic logic units(‘ALUs’). Each processing core (165) includes an ALU (166), and aseparate ALU (170) is dedicated to the exclusive use of the GlobalCombining Network Adapter (188) for use in performing the arithmetic andlogical functions of reduction operations, including an allreduceoperation. Computer program instructions of a reduction routine in aparallel communications library (161) may latch an instruction for anarithmetic or logical function into an instruction register (169). Whenthe arithmetic or logical function of a reduction operation is a ‘sum’or a ‘logical OR,’ for example, the collective operations adapter (188)may execute the arithmetic or logical operation by use of the ALU (166)in the processing core (165) or, typically much faster, by use of thededicated ALU (170) using data provided by the nodes (190, 192) on theglobal combining network (106) and data provided by processing cores(165) on the compute node (102).

Often when performing arithmetic operations in the global combiningnetwork adapter (188), however, the global combining network adapter(188) only serves to combine data received from the children nodes (190)and pass the result up the network (106) to the parent node (192).Similarly, the global combining network adapter (188) may only serve totransmit data received from the parent node (192) and pass the data downthe network (106) to the children nodes (190). That is, none of theprocessing cores (165) on the compute node (102) contribute data thatalters the output of ALU (170), which is then passed up or down theglobal combining network (106). Because the ALU (170) typically does notoutput any data onto the network (106) until the ALU (170) receivesinput from one of the processing cores (165), a processing core (165)may inject the identity element into the dedicated ALU (170) for theparticular arithmetic operation being perform in the ALU (170) in orderto prevent alteration of the output of the ALU (170). Injecting theidentity element into the ALU, however, often consumes numerousprocessing cycles. To further enhance performance in such cases, theexample compute node (102) includes dedicated hardware (171) forinjecting identity elements into the ALU (170) to reduce the amount ofprocessing core resources required to prevent alteration of the ALUoutput. The dedicated hardware (171) injects an identity element thatcorresponds to the particular arithmetic operation performed by the ALU.For example, when the global combining network adapter (188) performs abitwise OR on the data received from the children nodes (190), dedicatedhardware (171) may inject zeros into the ALU (170) to improveperformance throughout the global combining network (106).

For further explanation, FIG. 3A sets forth a block diagram of anexample Point-To-Point Adapter (180) useful in systems for initiating acollective operation in a parallel computer according to embodiments ofthe present invention. The Point-To-Point Adapter (180) is designed foruse in a data communications network optimized for point-to-pointoperations, a network that organizes compute nodes in athree-dimensional torus or mesh. The Point-To-Point Adapter (180) in theexample of FIG. 3A provides data communication along an x-axis throughfour unidirectional data communications links, to and from the next nodein the −x direction (182) and to and from the next node in the +xdirection (181). The Point-To-Point Adapter (180) of FIG. 3A alsoprovides data communication along a y-axis through four unidirectionaldata communications links, to and from the next node in the −y direction(184) and to and from the next node in the +y direction (183). ThePoint-To-Point Adapter (180) of FIG. 3A also provides data communicationalong a z-axis through four unidirectional data communications links, toand from the next node in the −z direction (186) and to and from thenext node in the +z direction (185).

For further explanation, FIG. 3B sets forth a block diagram of anexample Global Combining Network Adapter (188) useful in systems forinitiating a collective operation in a parallel computer according toembodiments of the present invention.

The Global Combining Network Adapter (188) is designed for use in anetwork optimized for collective operations, a network that organizescompute nodes of a parallel computer in a binary tree. The GlobalCombining Network Adapter (188) in the example of FIG. 3B provides datacommunication to and from children nodes of a global combining networkthrough four unidirectional data communications links (190), and alsoprovides data communication to and from a parent node of the globalcombining network through two unidirectional data communications links(192).

For further explanation, FIG. 4 sets forth a line drawing illustratingan example data communications network (108) optimized forpoint-to-point operations useful in systems capable of initiating acollective operation in a parallel computer according to embodiments ofthe present invention. In the example of FIG. 4, dots represent computenodes (102) of a parallel computer, and the dotted lines between thedots represent data communications links (103) between compute nodes.The data communications links are implemented with point-to-point datacommunications adapters similar to the one illustrated for example inFIG. 3A, with data communications links on three axis, x, y, and z, andto and fro in six directions +x (181), −x (182), +y (183), −y (184), +z(185), and −z (186). The links and compute nodes are organized by thisdata communications network optimized for point-to-point operations intoa three dimensional mesh (105). The mesh (105) has wrap-around links oneach axis that connect the outermost compute nodes in the mesh (105) onopposite sides of the mesh (105). These wrap-around links form a torus(107). Each compute node in the torus has a location in the torus thatis uniquely specified by a set of x, y, z coordinates. Readers will notethat the wrap-around links in the y and z directions have been omittedfor clarity, but are configured in a similar manner to the wrap-aroundlink illustrated in the x direction. For clarity of explanation, thedata communications network of FIG. 4 is illustrated with only 27compute nodes, but readers will recognize that a data communicationsnetwork optimized for point-to-point operations for use in initiating acollective operation in a parallel computer in accordance withembodiments of the present invention may contain only a few computenodes or may contain thousands of compute nodes. For ease ofexplanation, the data communications network of FIG. 4 is illustratedwith only three dimensions, but readers will recognize that a datacommunications network optimized for point-to-point operations for usein initiating a collective operation in a parallel computer inaccordance with embodiments of the present invention may in fact beimplemented in two dimensions, four dimensions, five dimensions, and soon. Several supercomputers now use five dimensional mesh or torusnetworks, including, for example, IBM's Blue Gene Q™.

For further explanation, FIG. 5 sets forth a line drawing illustratingan example global combining network (106) useful in systems capable ofinitiating a collective operation in a parallel computer according toembodiments of the present invention. The example data communicationsnetwork of FIG. 5 includes data communications links (103) connected tothe compute nodes so as to organize the compute nodes as a tree. In theexample of FIG. 5, dots represent compute nodes (102) of a parallelcomputer, and the dotted lines (103) between the dots represent datacommunications links between compute nodes. The data communicationslinks are implemented with global combining network adapters similar tothe one illustrated for example in FIG. 3B, with each node typicallyproviding data communications to and from two children nodes and datacommunications to and from a parent node, with some exceptions. Nodes inthe global combining network (106) may be characterized as a physicalroot node (202), branch nodes (204), and leaf nodes (206). The physicalroot (202) has two children but no parent and is so called because thephysical root node (202) is the node physically configured at the top ofthe binary tree. The leaf nodes (206) each has a parent, but leaf nodeshave no children. The branch nodes (204) each has both a parent and twochildren. The links and compute nodes are thereby organized by this datacommunications network optimized for collective operations into a binarytree (106). For clarity of explanation, the data communications networkof FIG. 5 is illustrated with only 31 compute nodes, but readers willrecognize that a global combining network (106) optimized for collectiveoperations for use in initiating a collective operation in a parallelcomputer in accordance with embodiments of the present invention maycontain only a few compute nodes or may contain thousands of computenodes.

In the example of FIG. 5, each node in the tree is assigned a unitidentifier referred to as a ‘rank’ (250). The rank actually identifies atask or process that is executing a parallel operation according toembodiments of the present invention. Using the rank to identify a nodeassumes that only one such task is executing on each node. To the extentthat more than one participating task executes on a single node, therank identifies the task as such rather than the node. A rank uniquelyidentifies a task's location in the tree network for use in bothpoint-to-point and collective operations in the tree network. The ranksin this example are assigned as integers beginning with 0 assigned tothe root tasks or root node (202), 1 assigned to the first node in thesecond layer of the tree, 2 assigned to the second node in the secondlayer of the tree, 3 assigned to the first node in the third layer ofthe tree, 4 assigned to the second node in the third layer of the tree,and so on. For ease of illustration, only the ranks of the first threelayers of the tree are shown here, but all compute nodes in the treenetwork are assigned a unique rank.

For further explanation, FIG. 6 sets forth a flow chart illustrating anexample method initiating a collective operation in a parallel computeraccording to embodiments of the present invention. In the method of FIG.6, the parallel computer includes a plurality of compute nodes that arecoupled for data communications by one or more data communicationsnetworks. The compute nodes are also organized in an operational groupfor collective operations with one compute node assigned as a root nodeof the operational group.

The method of FIG. 6 includes identifying (602), by a non-root computenode, a collective operation to execute in the operational group ofcompute nodes. Identifying (602), by a non-root compute node, acollective operation to execute in the operational group of computenodes may be carried out by discovering a condition during execution,where the condition is associated with a predefined collectiveoperation. In some embodiments, for example, compute nodes may reportalerts and events generated locally to other compute nodes. In someembodiments, such alerts and events may be associated with a predefinecollective operation such that upon occurrence of the alert or event, anon-root node is configured to initiate the predefined collectiveoperation.

The method of FIG. 6 also includes initiating (604), by the non-rootcompute node, execution of the collective operation amongst the computenodes of the operational group. In the method of FIG. 6, initiating(604), by the non-root compute node, execution of the collectiveoperation amongst the compute nodes of the operational group is carriedout by sending (606), by the non-root compute node to one or more of theother compute nodes in the operational group, an active message. Theactive message includes information configured to initiate execution ofthe collective operation amongst the compute nodes of the operationalgroup. Such information may include a handle of a function to execute,data upon which to perform an operation, executable instructions, acallback variable, and other information as will occur to readers ofskill in the art.

The method of FIG. 6 also includes executing (610), by the compute nodesof the operational group, the collective operation. Executing (610) thecollective operation may be carried out by performing operationsspecified in the active message, sending data specified in the activemessage to compute nodes specified in the active message, and in otherways as will occur to readers of skill in the art.

For further explanation, FIG. 7 sets forth a flow chart illustratinganother example method initiating a collective operation in a parallelcomputer according to embodiments of the present invention. The methodof FIG. 7 is similar to the method of FIG. 6 in that the method of FIG.7 also includes identifying (602) a collective operation, initiating(604) execution of the collective operation by sending (606) an activemessage, and executing (608) the collective operation.

The method of FIG. 7 differs from the method of FIG. 6, however, in thatin the method of FIG. 7, the compute nodes of the operational group areconfigured in a tree topology and initiating (604) execution of thecollective operation includes initiating (702) execution of a broadcastoperation. In the method of FIG. 7 initiating (702) execution of abroadcast operation is carried out by sending (704) by the non-rootcompute node to the root compute node an active message instructing theroot node to send, to each of the root node's children, contributiondata and another instruction for the root node's children to forward thesame contribution data and the same instruction to the children of theroot node's children. Effectively, the non-root compute node causes theroot node to effect a broadcast operation in the tree by passing theroot node passing down an instruction to the root's children to forwardcontribution data and forward the instruction to the children'schildren. By passing down both the contribution data and instruction,the broadcast operation is effectively self-propagating through thetree. Consider, the following example broadcast operation initiated inaccordance with embodiment of the present invention:

-   -   a root node receives the active message from the non-root        compute node;    -   the root node forwards contribution data to two child nodes;    -   with the contribution data, the root node also forwards an        instruction to carry out the same steps (forward contribution        data and the instruction);    -   Each child node forwards the contribution data to the node's        children along with an instruction to do the same; and    -   the process of forwarding the instruction and contribution data        repeats for each node until the leaf nodes at the bottom of the        tree have received the contribution data of their parents.

In some embodiment, the contribution data broadcast down the treerepresents a notification (originating from the non-root compute node)to cease sending error messages. Consider, for example, that manycompute nodes in the operational group experience the same error andreport the same error to the non-root compute node. Once the error hasbeen handled (logged or the like), any further error messages aresuperfluous and only increase data traffic on the network. As such, thenon-root compute node may initiate a broadcast operation where thecontribution data is a notification to cease sending error messages.

For further explanation, FIG. 8 sets forth a flow chart illustratinganother example method initiating a collective operation in a parallelcomputer according to embodiments of the present invention. The methodof FIG. 8 is similar to the method of FIG. 6 in that the method of FIG.8 also includes identifying (602) a collective operation, initiating(604) execution of the collective operation by sending (606) an activemessage, and executing (608) the collective operation.

The method of FIG. 8 differs from the method of FIG. 6, however, in thatin the method of FIG. 8, the compute nodes of the operational group areconfigured in a tree topology and initiating (604) execution of thecollective operation includes initiating (702) execution of a reduceoperation. In the method of FIG. 7, initiating (702) execution of areduce operation is carried out by sending (704), by the non-rootcompute node to each of the leaf nodes positioned at the bottom of thetree topology, an active message instructing each of the leaf nodes toforward data to the leaf node's parent, perform an operation on thedata, and forward an instruction to the leaf node's parent to carry outthe same steps.

In this example, the reduction operation may effect event consolidationor event suppression in the operational group of compute nodes. Once anevent is identified, a non-root node may be configured to suppressrepetition of the event in logging throughout a tree or consolidateseveral events into a single event for logging in the tree. Suchconsolidation and suppression may be carried out through a reductionoperation initiated by the non-root node in accordance with embodimentsof the present invention.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readabletransmission medium or a computer readable storage medium. A computerreadable storage medium may be, for example, but not limited to, anelectronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer readable storage medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer readable storage medium may be any tangible medium that cancontain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer readable transmission medium may include a propagated datasignal with computer readable program code embodied therein, forexample, in baseband or as part of a carrier wave. Such a propagatedsignal may take any of a variety of forms, including, but not limitedto, electro-magnetic, optical, or any suitable combination thereof. Acomputer readable transmission medium may be any computer readablemedium that is not a computer readable storage medium and that cancommunicate, propagate, or transport a program for use by or inconnection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

It will be understood from the foregoing description that modificationsand changes may be made in various embodiments of the present inventionwithout departing from its true spirit. The descriptions in thisspecification are for purposes of illustration only and are not to beconstrued in a limiting sense. The scope of the present invention islimited only by the language of the following claims.

What is claimed is:
 1. A method of initiating a collective operation ina parallel computer, the parallel computer comprising a plurality ofcompute nodes, the compute nodes coupled for data communications by oneor more data communications networks, the compute nodes organized in anoperational group for collective operations with one compute nodeassigned as a root node of the operational group, the method comprising:identifying, by a non-root compute node, a collective operation toexecute in the operational group of compute nodes; initiating, by thenon-root compute node, execution of the collective operation amongst thecompute nodes of the operational group including: sending, by thenon-root compute node to one or more of the other compute nodes in theoperational group, an active message, the active message comprisinginformation configured to initiate execution of the collective operationamongst the compute nodes of the operational group; and executing, bythe compute nodes of the operational group, the collective operation. 2.The method of claim 1, wherein: the compute nodes of the operationalgroup are configured in a tree topology; initiating execution of thecollective operation further comprises initiating execution of abroadcast operation; and sending the active message to one or more ofthe other compute nodes further comprises sending by the non-rootcompute node to the root compute node an active message instructing theroot node to send, to each of the root node's children, contributiondata and another instruction for the root node's children to forward thesame contribution data and the same instruction to the children of theroot node's children.
 3. The method of claim 2, wherein the contributiondata represents a notification to cease sending error messages.
 4. Themethod of claim 1, wherein: the compute nodes of the operational groupare configured in a tree topology; initiating execution of thecollective operation further comprises initiating execution of a reduceoperation; and sending the active message to one or more of the othercompute nodes further comprises sending, by the non-root compute node toeach of the leaf nodes positioned at the bottom of the tree topology, anactive message instructing each of the leaf nodes to forward data to theleaf node's parent, perform an operation on the data, and forward aninstruction to the leaf node's parent to carry out the same steps. 5.The method of claim 4, wherein the reduction operation effects eventconsolidation in the operational group of compute nodes.
 6. The methodof claim 4, wherein the reduction operation effects event suppression inthe operational group of compute nodes.
 7. An apparatus for initiating acollective operation in a parallel computer, the parallel computercomprising a plurality of compute nodes, the compute nodes coupled fordata communications by one or more data communications networks, thecompute nodes organized in an operational group for collectiveoperations with one compute node assigned as a root node of theoperational group, the apparatus comprising a computer processor, acomputer memory operatively coupled to the computer processor, thecomputer memory having disposed within it computer program instructionsthat, when executed by the computer processor, cause the apparatus tocarry out the steps of: identifying, by a non-root compute node, acollective operation to execute in the operational group of computenodes; initiating, by the non-root compute node, execution of thecollective operation amongst the compute nodes of the operational groupincluding: sending, by the non-root compute node to one or more of theother compute nodes in the operational group, an active message, theactive message comprising information configured to initiate executionof the collective operation amongst the compute nodes of the operationalgroup; and executing, by the compute nodes of the operational group, thecollective operation.
 8. The apparatus of claim 7, wherein: the computenodes of the operational group are configured in a tree topology;initiating execution of the collective operation further comprisesinitiating execution of a broadcast operation; and sending the activemessage to one or more of the other compute nodes further comprisessending by the non-root compute node to the root compute node an activemessage instructing the root node to send, to each of the root node'schildren, contribution data and another instruction for the root node'schildren to forward the same contribution data and the same instructionto the children of the root node's children.
 9. The apparatus of claim8, wherein the contribution data represents a notification to ceasesending error messages.
 10. The apparatus of claim 7, wherein: thecompute nodes of the operational group are configured in a treetopology; initiating execution of the collective operation furthercomprises initiating execution of a reduce operation; and sending theactive message to one or more of the other compute nodes furthercomprises sending, by the non-root compute node to each of the leafnodes positioned at the bottom of the tree topology, an active messageinstructing each of the leaf nodes to forward data to the leaf node'sparent, perform an operation on the data, and forward an instruction tothe leaf node's parent to carry out the same steps.
 11. The apparatus ofclaim 10, wherein the reduction operation effects event consolidation inthe operational group of compute nodes.
 12. The apparatus of claim 10,wherein the reduction operation effects event suppression in theoperational group of compute nodes.
 13. A computer program product forinitiating a collective operation in a parallel computer, the parallelcomputer comprising a plurality of compute nodes, the compute nodescoupled for data communications by one or more data communicationsnetworks, the compute nodes organized in an operational group forcollective operations with one compute node assigned as a root node ofthe operational group, the computer program product disposed upon acomputer readable medium, the computer program product comprisingcomputer program instructions that, when executed, cause a computer tocarry out the steps of: identifying, by a non-root compute node, acollective operation to execute in the operational group of computenodes; initiating, by the non-root compute node, execution of thecollective operation amongst the compute nodes of the operational groupincluding: sending, by the non-root compute node to one or more of theother compute nodes in the operational group, an active message, theactive message comprising information configured to initiate executionof the collective operation amongst the compute nodes of the operationalgroup; and executing, by the compute nodes of the operational group, thecollective operation.
 14. The computer program product of claim 13,wherein: the compute nodes of the operational group are configured in atree topology; initiating execution of the collective operation furthercomprises initiating execution of a broadcast operation; and sending theactive message to one or more of the other compute nodes furthercomprises sending by the non-root compute node to the root compute nodean active message instructing the root node to send, to each of the rootnode's children, contribution data and another instruction for the rootnode's children to forward the same contribution data and the sameinstruction to the children of the root node's children.
 15. Thecomputer program product of claim 14, wherein the contribution datarepresents a notification to cease sending error messages.
 16. Thecomputer program product of claim 13, wherein: the compute nodes of theoperational group are configured in a tree topology; initiatingexecution of the collective operation further comprises initiatingexecution of a reduce operation; and sending the active message to oneor more of the other compute nodes further comprises sending, by thenon-root compute node to each of the leaf nodes positioned at the bottomof the tree topology, an active message instructing each of the leafnodes to forward data to the leaf node's parent, perform an operation onthe data, and forward an instruction to the leaf node's parent to carryout the same steps.
 17. The computer program product of claim 16,wherein the reduction operation effects event consolidation in theoperational group of compute nodes.
 18. The computer program product ofclaim 16, wherein the reduction operation effects event suppression inthe operational group of compute nodes.
 19. The computer program productof claim 13 wherein the computer readable medium comprises a signalmedium.
 20. The computer program product of claim 13 wherein thecomputer readable medium comprises a storage medium.